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Many data are interpreted to evaluate a petroleum-bearing formation, and we discuss the interpretations of data acquired through surface data logging in terms of the rock formation and fluid properties they help determine. The logging engineer or geologist gets information about the formation fluids directly from fluids that are released into the wellbore while drilling and circulating out suspended immiscibly in the drill fluid or remaining in the pores of larger cuttings that may not have been flushed. They receive information indirectly from remnants of the fluid that remain in pores of rock cuttings, as stains on the grain surface, or in solution in the drilling fluid. Oil may be identified as a sheen on the surface of water-based drilling fluid. If the circulating fluid density is sufficiently low as to render an underbalanced drilling condition, oil may be produced in large enough quantities that a sample may be skimmed off a whole mud sample.
Algorithms are taking over the world, or so we are led to believe, given their growing pervasiveness in multiple fields of human endeavor such as consumer marketing, finance, design and manufacturing, health care, politics, sports, etc. The focus of this article is to examine where things stand in regard to the application of these techniques for managing subsurface energy resources in domains such as conventional and unconventional oil and gas, geologic carbon sequestration, and geothermal energy. It is useful to start with some definitions to establish a common vocabulary. Thus, DA can be thought of as a broad framework that helps determine what happened (descriptive analytics), why it happened (diagnostic analytics), what will happen (predictive analytics), or how can we make something happen (prescriptive analytics) (Sankaran et al. 2019). Although DA is built upon a foundation of classical statistics and optimization, it has increasingly come to rely upon ML, especially for predictive and prescriptive analytics (Donoho 2017).
The purpose of the digital oilfield is to maximize oilfield recovery, eliminate non-productive time, and increase profitability through the design and deployment of integrated workflows. Digital oilfield workflows combine business process management with advanced information technology and engineering expertise to streamline and, in many cases, automate the execution of tasks performed by cross-functional teams. The term "digital oilfield" has been used to describe a wide variety of activities, and its definitions have encompassed an equally wide variety of tools, tasks, and disciplines. All of them attempt to describe various uses of advanced software and data analysis techniques to improve the profitability of oil & gas production operations. If one maps the challenges onto the themes, it becomes clear that digital oilfields are attempting to compensate for a higher complexity and cost of operations which must be performed by fewer, less experienced employees.
Their mission is to ensure that the company's technical team is increasingly able to use advanced data analysis to find and produce oil and gas more productively. The focus is on increasing the capabilities of those with traditional engineering and geology training. "Someone who can understand seismic processing can program a neural network," said Patrick von Pattay, a vice president for Wintershall Dea and chairman off the Digital Transformation Committee of SPE's Digital Energy Technical Section. That is an apt description of Dillen, whose work as a geophysicist using advanced analytics led to his current job. In both roles, finding new ways to extract useful bits of information from massive data sets is valuable.
Andrew Bruce's path to building a digital business offers a map of hazards for those selling digital services to oil companies. When he started Data Gumbo, he was thinking about building a business aimed at solving the data quality issues that were a constant headache when he was working on developing digital control systems at NOV. One option was a fee-for-service business that cleaned up drilling data. While he knew that would make the engineers happy, he wondered if accountants would notice. Clean data ultimately can have a large financial impact by facilitating the digital transformation.
Oil industry executives surveyed last year ranked the potential positive impact of big data analytics at the top of the list of trends, higher than even changes in oil demand. That bold conclusion was from a survey by accounting firm Ernst and Young (EY), putting big data analytics among the top trends that could aid business growth in the next 3 years, even above the demand swings that move oil prices. The survey may have reflected the mood last summer when the outlook for oil consumption looked so weak that cost saving was the only path to better results. "The survey speaks to a high-level ambition across the operator community to use digital as a mechanism to drive down costs," said Toby Summers executive director for EY. The promise there is that digital can allow them to scale up operations with fewer hires in good times and scale back with fewer layoffs when the cycle turns down. These projects also cost less than other cost-cutting options.
The complete paper describes challenges that must be overcome to reach the goal of drilling systems automation (DSA). The authors explore steps necessary to realize the full potential of performance-enhancing functionalities in automated drilling control (ADC) software, highlight current gaps, and present relatively easily achievable goals that can enable significant cost reduction and improvements in automation and safety. They also emphasize that automation is a multidisciplinary task, and that success requires collaboration between different sectors of the drilling industry. The 19-page complete paper includes detailed technical discussion of topics ranging from the basic principles of an ADC system and practical challenges experienced with a model-based digital twin approach to suggested solutions and improvements. Each topic is divided into numerous related discussions.
A recent webinar by the SPE Gulf Coast Section Data Analytics Study Group explored the role of analytics in today's industry and the opportunities it can bring to oil and gas professionals. The key is to think about your capabilities as multipurpose. In the future we need to expand the way we see the subsurface. Three industry speakers approached the topic from different angles. Susan Nash, director of innovation, emerging science, and technology at American Association of Petroleum Geologists, discussed the permeation of analytics across the industry's verticals--upstream, midstream, and downstream.